A collection of resources and commentary providing an introduction to supply chain management and related systems for students, practitioners, and anyone else interested in learning more about how to design, manufacture, transport, store, deliver, and manage products.

Tuesday, February 5, 2013

Demand Driven Supply Chain: No more the Holy Grail of Operations Management.

The article “Cleaning the Crystal Ball” discusses about the
necessity to establish a clear forecasting mechanism to be able to plan the
operations in a particular direction. The reason for forecast year after year
even though they happen to be incorrect is that they lend direction in an
otherwise intuition driven decision making. Now that at least we have a solid inference
drawn from data analysis to base and evaluate our intuition driven decisions.

Though there is no denying in that the forecast mechanisms
have to develop before they can be claimed dependable, the maturity levels
attained now would be of better use if we could integrate forecasting and data
analysis to the inputs of a company’s quarter on quarter strategy planning.
Most companies fail to derive the complete benefits of forecasting. Objective
forecasting to arrive at one or range of numbers is parallel to choosing to
walk with one eye when all one has to do is open the other eye for better
visibility.

No one model can actually yield best results for all the
organizations. The forecasting models have to be developed from within the
organization by incorporating the error quotient and monitoring the forecast
against the actual results. Incorporating more factors that were not considered
earlier would strengthen the forecast mechanism though at the cost of
complexity. Demand forecasting in most cases leaves the team with a range of numbers.
They sometimes make people see patterns that could have otherwise escaped the
minds engrossed in other directions. There are reasons for this distribution of
numbers, rather than focusing on the demerits and complaining about the flaws
in forecast and one could benefit by accepting that the forecasting mechanism
is yet to reach a level of maturity and focusing on

What factors influence the range positively/negatively?

What factors are most sensitive to output?

What drivers can we control/monitor to adapt to
the situation?

What are the key indicators for us to focus on
to reaffirm our strategic position every week (or month)?

How would my cash flow look like at each of
these ranges?

Establishing a contingency plan for threats that
are assigned priority after both qualitative and quantitative analysis.

Besides all this, a company can somehow get to form a team
with people from various domains like the Sales, Supply Chain Management,
Marketing and Strategy planning and establish an agile process in place to help
the suppliers and manufacturers cut down the response time taken. This could
effectively save a lot of inventory cost and waste especially in the Consumable
Goods Industry. While it could even take decades for the forecast models to
reach the expected levels of accuracy, the companies that take leverage of the
existing advantages of forecasting would have a strategically competitive
position and less things to catch-up later when the model matures. It is
interesting to realize how the article “Four Steps to Forecast Total Market
Demand” starts emphasizing the importance of the byproducts of forecasting and
sort of resonates with our discussion thus far.

It would be interesting to ask ourselves “Shouldn’t companies
focus on deriving best out of existing provisions to continuously adapt as the
forecast model develops?”